APSO based automated planning in Constructive Simulation

Constructive simulations are the applications used by the military for the training of their commanders in planning and analysis of various threats and Courses of Action. In the ‘analysis wargames’, there are need to automate many of the tasks of the commander which are carried out by subunit commanders on the ground. Deployment of defence units is one of such important decision making by commander. Deployments of units (and sub units) is dependent on multiple factors which needs to be satisfied/optimised for meeting the given objective of the unit. In this paper we have attempted to solve the multi criterion decision problem of optimal deployment of defence units in mountainous terrain using Particle Swarm Optimization(PSO) and Adaptive Particle Swarm Optimization(APSO). The algorithm has been tested with varied number of decision parameters and their weights using digital elevation and vector data of the terrain features. The auto deployment outcomes are found satisfactory. Our solution approach has potential in automated planning in constructive simulations.   &nbsp

Knowledge, anxiety and the use of hydroxychloroquine prophylaxis among health care students and professionals regarding COVID-19 pandemic

Introduction:  Data regarding knowledge and attitude about COVID-19, the prevalence of acceptance of hydroxychloroquine prophylaxis and anxiety amidst COVID-19 pandemic among health care students/professionals in India is scarce.Material and methods: A cross-sectional study was conducted during May 2020, using an online survey via Google forms. A self-administered validated structured questionnaire was applied, which comprised 28 questions among health care students/professionals at a tertiary care centrein North India.Results: A total of 956 respondents were included (10.2% nurses, 45.2% medical students, 24.3% paramedical students, 11.7% resident doctors and 8.6% consultant doctors). Overall knowledge score was 9.3/15; the highest for preventive practices (4/5), followed by clinical knowledge (2.7/5) and the use of personal protective equipment (PPE) (2.6/5). The overall score was the highest in consultant doctors (10.8) while the lowest in nurses (8.5) and paramedical students (8.4) (p < 0.001). Less than half of the respondents had knowledge about the correct sequence of doffing PPE and the use of N95 mask. About 21.8% of the participants experienced moderate to severe anxiety; higher among nurses (38%), followed by paramedical students (29.3%); and anxiety was higher when knowledge score was low (27.6% vs 14.7%); both factors were independent predictors on multivariate analysis (p < 0.001). Only 18.1% of the respondents applied HCQ prophylaxis — the highest proportion constituted consultants (42.7%), and the least — paramedical students (5.2%); (p < 0.001) and HCQ use was more frequently used if they had a family member of extreme age group at home (23.3% vs 12.2%; p < 0.001). Conclusions: The knowledge about correct PPE usage is low among all groups of HCWs and students, and there is a high prevalence of anxiety due to COVID-19. The lower COVID-19 knowledge scores were significantly associated with a higher likelihood of anxiety and inadequate use of HCQ prophylaxis. The appliance of HCQ prophylaxis had no significant association with anxiety levels of the respondents

Preemptive MACO (MACO-P) Algorithm for Reducing Travel Time in VANETs

Vehicular Ad-hoc NETworks (VANETs) are extremely flexible and dynamic ad-hoc networks that are used to provide smooth, safe and comfortable journey to commuters. The commuters spend significant time of their journey either for their turn to cross the intersections or in the congestion on the path. For reducing the total travel time, it is essential to minimize the waiting time at intersections and find best/optimal congestion-free paths for smoother movement of vehicular traffic on roads. A novel Preemptive Modified Ant Colony Optimization (MACO-P) algorithm has been proposed in this paper for reducing the total travel time. The Modified Ant Colony Optimization (MACO) algorithm is used in literature to avoid the congested path by sensing the pheromone trail. Adding preemption to the existing MACO algorithm will result in the reduction of the average queue length at intersections, meaning thereby, less waiting time ensuring smooth mobility of vehicles. For implementation, various open source softwares, i.e., OSM, Simulation of Urban MObility (SUMO), MObility model generator for VEhicular networks (MOVE), Python and Traffic Control Interface (TraCI) are being used. Real-time maps are being fetched by OSM. Traffic simulation is done using SUMO and the Mobility model is generated through MOVE. Python is used for writing client scripts that initiate and control the simulation, with traffic control interface provided by TraCI. Experimental results confirm that the the total travel time is reduced using the proposed MACO-P algorithm, resulting in significant reduction in consumption of fuel